predictive modeling

A predictive model is made up of a number of predictors, which are variable factors that are likely to influence future behavior or results. In marketing, for example, a customer's gender, age, and purchase history might predict the likelihood of a future sale.

In predictive modeling, data is collected for the relevant predictors, a statistical model is formulated, predictions are made and the model is validated (or revised) as additional data becomes available. The model may employ a simple linear equation or a complex neural network, mapped out by sophisticated software.

Predictive modeling is used widely in information technology (IT). In spam filtering systems, for example, predictive modeling is sometimes used to identify the probability that a given message is spam. Other applications of predictive modeling include customer relationship management (CRM), capacity planning, change management, disaster recovery, security management , engineering, meteorology and city planning.

Learn more about Data modeling tools and techniques

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More resources from around the web: - Ed Tittel discusses predictive modeling for capacity planning. - Eric Siegel explains predictive analytics for CRM. - The Open Data Group includes information about predictive modeling in its data mining FAQ list.

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